Overview

Dataset statistics

Number of variables14
Number of observations321434
Missing cells48654
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.3 MiB
Average record size in memory112.0 B

Variable types

Categorical4
DateTime1
Numeric9

Alerts

VERSIE has constant value ""Constant
DATUM_BESTAND has constant value ""Constant
PEILDATUM has constant value ""Constant
TYPERENDE_DIAGNOSE_CD has a high cardinality: 1899 distinct valuesHigh cardinality
BEHANDELEND_SPECIALISME_CD is highly overall correlated with AANTAL_PAT_PER_SPCHigh correlation
AANTAL_PAT_PER_ZPD is highly overall correlated with AANTAL_SUBTRAJECT_PER_ZPDHigh correlation
AANTAL_SUBTRAJECT_PER_ZPD is highly overall correlated with AANTAL_PAT_PER_ZPDHigh correlation
AANTAL_PAT_PER_DIAG is highly overall correlated with AANTAL_SUBTRAJECT_PER_DIAGHigh correlation
AANTAL_SUBTRAJECT_PER_DIAG is highly overall correlated with AANTAL_PAT_PER_DIAGHigh correlation
AANTAL_PAT_PER_SPC is highly overall correlated with BEHANDELEND_SPECIALISME_CD and 1 other fieldsHigh correlation
AANTAL_SUBTRAJECT_PER_SPC is highly overall correlated with AANTAL_PAT_PER_SPCHigh correlation
GEMIDDELDE_VERKOOPPRIJS has 48654 (15.1%) missing valuesMissing
AANTAL_SUBTRAJECT_PER_ZPD is highly skewed (γ1 = 21.09481489)Skewed

Reproduction

Analysis started2023-03-28 08:14:46.667076
Analysis finished2023-03-28 08:15:08.469658
Duration21.8 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

VERSIE
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
1.0
321434 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters964302
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 321434
100.0%

Length

2023-03-28T08:15:08.537603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T08:15:08.677918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 321434
100.0%

Most occurring characters

ValueCountFrequency (%)
1 321434
33.3%
. 321434
33.3%
0 321434
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 642868
66.7%
Other Punctuation 321434
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 321434
50.0%
0 321434
50.0%
Other Punctuation
ValueCountFrequency (%)
. 321434
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 964302
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 321434
33.3%
. 321434
33.3%
0 321434
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 964302
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 321434
33.3%
. 321434
33.3%
0 321434
33.3%

DATUM_BESTAND
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
2023-03-10
321434 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3214340
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-03-10
2nd row2023-03-10
3rd row2023-03-10
4th row2023-03-10
5th row2023-03-10

Common Values

ValueCountFrequency (%)
2023-03-10 321434
100.0%

Length

2023-03-28T08:15:08.789910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T08:15:08.927461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2023-03-10 321434
100.0%

Most occurring characters

ValueCountFrequency (%)
0 964302
30.0%
2 642868
20.0%
3 642868
20.0%
- 642868
20.0%
1 321434
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2571472
80.0%
Dash Punctuation 642868
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 964302
37.5%
2 642868
25.0%
3 642868
25.0%
1 321434
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 642868
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3214340
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 964302
30.0%
2 642868
20.0%
3 642868
20.0%
- 642868
20.0%
1 321434
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3214340
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 964302
30.0%
2 642868
20.0%
3 642868
20.0%
- 642868
20.0%
1 321434
 
10.0%

PEILDATUM
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
2023-03-01
321434 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3214340
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-03-01
2nd row2023-03-01
3rd row2023-03-01
4th row2023-03-01
5th row2023-03-01

Common Values

ValueCountFrequency (%)
2023-03-01 321434
100.0%

Length

2023-03-28T08:15:09.039114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T08:15:09.176550image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2023-03-01 321434
100.0%

Most occurring characters

ValueCountFrequency (%)
0 964302
30.0%
2 642868
20.0%
3 642868
20.0%
- 642868
20.0%
1 321434
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2571472
80.0%
Dash Punctuation 642868
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 964302
37.5%
2 642868
25.0%
3 642868
25.0%
1 321434
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 642868
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3214340
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 964302
30.0%
2 642868
20.0%
3 642868
20.0%
- 642868
20.0%
1 321434
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3214340
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 964302
30.0%
2 642868
20.0%
3 642868
20.0%
- 642868
20.0%
1 321434
 
10.0%

JAAR
Date

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
Minimum2012-01-01 00:00:00
Maximum2023-01-01 00:00:00
2023-03-28T08:15:09.275435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:09.397346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean438.46217
Minimum301
Maximum8418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-03-28T08:15:09.549078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum301
5-th percentile302
Q1305
median313
Q3322
95-th percentile335
Maximum8418
Range8117
Interquartile range (IQR)17

Descriptive statistics

Standard deviation990.3148
Coefficient of variation (CV)2.2586095
Kurtosis60.821646
Mean438.46217
Median Absolute Deviation (MAD)8
Skewness7.9205314
Sum1.4093665 × 108
Variance980723.4
MonotonicityNot monotonic
2023-03-28T08:15:09.704335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
305 45451
14.1%
313 41599
12.9%
303 37029
11.5%
330 25407
 
7.9%
316 21881
 
6.8%
308 17400
 
5.4%
306 13484
 
4.2%
324 13276
 
4.1%
301 12850
 
4.0%
304 10414
 
3.2%
Other values (18) 82643
25.7%
ValueCountFrequency (%)
301 12850
 
4.0%
302 7028
 
2.2%
303 37029
11.5%
304 10414
 
3.2%
305 45451
14.1%
306 13484
 
4.2%
307 5607
 
1.7%
308 17400
 
5.4%
310 3502
 
1.1%
313 41599
12.9%
ValueCountFrequency (%)
8418 4291
 
1.3%
8416 576
 
0.2%
1900 210
 
0.1%
390 864
 
0.3%
389 3397
 
1.1%
362 4328
 
1.3%
361 2313
 
0.7%
335 3243
 
1.0%
330 25407
7.9%
329 835
 
0.3%
Distinct1899
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
101
 
1363
402
 
1314
301
 
1285
403
 
1282
201
 
1209
Other values (1894)
314981 

Length

Max length4
Median length3
Mean length3.3529838
Min length2

Characters and Unicode

Total characters1077763
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)< 0.1%

Sample

1st row19
2nd row19
3rd row19
4th row03
5th row03

Common Values

ValueCountFrequency (%)
101 1363
 
0.4%
402 1314
 
0.4%
301 1285
 
0.4%
403 1282
 
0.4%
201 1209
 
0.4%
203 1204
 
0.4%
401 1071
 
0.3%
404 1065
 
0.3%
802 1047
 
0.3%
409 1035
 
0.3%
Other values (1889) 309559
96.3%

Length

2023-03-28T08:15:09.884273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
101 1363
 
0.4%
402 1314
 
0.4%
301 1285
 
0.4%
403 1282
 
0.4%
201 1209
 
0.4%
203 1204
 
0.4%
401 1071
 
0.3%
404 1065
 
0.3%
802 1047
 
0.3%
409 1035
 
0.3%
Other values (1889) 309559
96.3%

Most occurring characters

ValueCountFrequency (%)
1 206266
19.1%
0 197520
18.3%
2 142905
13.3%
3 116689
10.8%
5 83168
7.7%
9 77631
 
7.2%
4 76369
 
7.1%
7 63516
 
5.9%
6 56364
 
5.2%
8 46439
 
4.3%
Other values (15) 10896
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1066867
99.0%
Uppercase Letter 10896
 
1.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 2022
18.6%
M 1844
16.9%
B 1306
12.0%
E 920
8.4%
Z 915
8.4%
D 731
 
6.7%
A 707
 
6.5%
F 679
 
6.2%
C 361
 
3.3%
K 350
 
3.2%
Other values (5) 1061
9.7%
Decimal Number
ValueCountFrequency (%)
1 206266
19.3%
0 197520
18.5%
2 142905
13.4%
3 116689
10.9%
5 83168
7.8%
9 77631
 
7.3%
4 76369
 
7.2%
7 63516
 
6.0%
6 56364
 
5.3%
8 46439
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1066867
99.0%
Latin 10896
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 2022
18.6%
M 1844
16.9%
B 1306
12.0%
E 920
8.4%
Z 915
8.4%
D 731
 
6.7%
A 707
 
6.5%
F 679
 
6.2%
C 361
 
3.3%
K 350
 
3.2%
Other values (5) 1061
9.7%
Common
ValueCountFrequency (%)
1 206266
19.3%
0 197520
18.5%
2 142905
13.4%
3 116689
10.9%
5 83168
7.8%
9 77631
 
7.3%
4 76369
 
7.2%
7 63516
 
6.0%
6 56364
 
5.3%
8 46439
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1077763
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 206266
19.1%
0 197520
18.3%
2 142905
13.3%
3 116689
10.8%
5 83168
7.7%
9 77631
 
7.2%
4 76369
 
7.1%
7 63516
 
5.9%
6 56364
 
5.2%
8 46439
 
4.3%
Other values (15) 10896
 
1.0%

ZORGPRODUCT_CD
Real number (ℝ)

Distinct6031
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4246152 × 108
Minimum10501002
Maximum9.9841808 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-03-28T08:15:10.064393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum10501002
5-th percentile28999040
Q199899013
median1.49899 × 108
Q39.90004 × 108
95-th percentile9.9051604 × 108
Maximum9.9841808 × 108
Range9.8791708 × 108
Interquartile range (IQR)8.9010499 × 108

Descriptive statistics

Standard deviation4.2936081 × 108
Coefficient of variation (CV)0.9703913
Kurtosis-1.7450632
Mean4.4246152 × 108
Median Absolute Deviation (MAD)1.2 × 108
Skewness0.45955627
Sum1.4222218 × 1014
Variance1.8435071 × 1017
MonotonicityNot monotonic
2023-03-28T08:15:10.252877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
990004009 2325
 
0.7%
990004007 2290
 
0.7%
990003004 2253
 
0.7%
990004006 1874
 
0.6%
990356076 1692
 
0.5%
990356073 1569
 
0.5%
131999228 1493
 
0.5%
131999164 1476
 
0.5%
990003007 1466
 
0.5%
131999194 1354
 
0.4%
Other values (6021) 303642
94.5%
ValueCountFrequency (%)
10501002 9
< 0.1%
10501003 11
< 0.1%
10501004 11
< 0.1%
10501005 11
< 0.1%
10501007 3
 
< 0.1%
10501008 11
< 0.1%
10501010 11
< 0.1%
10501011 3
 
< 0.1%
11101002 10
< 0.1%
11101003 11
< 0.1%
ValueCountFrequency (%)
998418081 162
0.1%
998418080 146
< 0.1%
998418079 38
 
< 0.1%
998418077 8
 
< 0.1%
998418076 8
 
< 0.1%
998418075 7
 
< 0.1%
998418074 214
0.1%
998418073 215
0.1%
998418072 8
 
< 0.1%
998418071 8
 
< 0.1%

AANTAL_PAT_PER_ZPD
Real number (ℝ)

Distinct10202
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean523.6785
Minimum1
Maximum165140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-03-28T08:15:10.440676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median14
Q3106
95-th percentile1784
Maximum165140
Range165139
Interquartile range (IQR)103

Descriptive statistics

Standard deviation3211.7908
Coefficient of variation (CV)6.1331348
Kurtosis396.07661
Mean523.6785
Median Absolute Deviation (MAD)13
Skewness16.485649
Sum1.6832807 × 108
Variance10315600
MonotonicityNot monotonic
2023-03-28T08:15:10.780705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 52710
 
16.4%
2 25846
 
8.0%
3 16873
 
5.2%
4 12351
 
3.8%
5 9682
 
3.0%
6 8231
 
2.6%
7 6820
 
2.1%
8 5755
 
1.8%
9 5209
 
1.6%
10 4692
 
1.5%
Other values (10192) 173265
53.9%
ValueCountFrequency (%)
1 52710
16.4%
2 25846
8.0%
3 16873
 
5.2%
4 12351
 
3.8%
5 9682
 
3.0%
6 8231
 
2.6%
7 6820
 
2.1%
8 5755
 
1.8%
9 5209
 
1.6%
10 4692
 
1.5%
ValueCountFrequency (%)
165140 1
< 0.1%
162004 1
< 0.1%
155884 1
< 0.1%
154269 1
< 0.1%
154268 1
< 0.1%
144724 1
< 0.1%
118397 1
< 0.1%
115938 1
< 0.1%
112534 1
< 0.1%
111204 1
< 0.1%

AANTAL_SUBTRAJECT_PER_ZPD
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct10907
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean619.17139
Minimum1
Maximum240002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-03-28T08:15:10.968803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median15
Q3116
95-th percentile2035
Maximum240002
Range240001
Interquartile range (IQR)113

Descriptive statistics

Standard deviation4133.9611
Coefficient of variation (CV)6.6766023
Kurtosis708.09274
Mean619.17139
Median Absolute Deviation (MAD)14
Skewness21.094815
Sum1.9902274 × 108
Variance17089635
MonotonicityNot monotonic
2023-03-28T08:15:11.157764image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 50712
 
15.8%
2 25400
 
7.9%
3 16709
 
5.2%
4 12147
 
3.8%
5 9582
 
3.0%
6 8203
 
2.6%
7 6808
 
2.1%
8 5691
 
1.8%
9 5190
 
1.6%
10 4660
 
1.4%
Other values (10897) 176332
54.9%
ValueCountFrequency (%)
1 50712
15.8%
2 25400
7.9%
3 16709
 
5.2%
4 12147
 
3.8%
5 9582
 
3.0%
6 8203
 
2.6%
7 6808
 
2.1%
8 5691
 
1.8%
9 5190
 
1.6%
10 4660
 
1.4%
ValueCountFrequency (%)
240002 1
< 0.1%
232423 1
< 0.1%
231954 1
< 0.1%
231005 1
< 0.1%
227936 1
< 0.1%
227409 1
< 0.1%
226301 1
< 0.1%
223939 1
< 0.1%
218673 1
< 0.1%
215068 1
< 0.1%

AANTAL_PAT_PER_DIAG
Real number (ℝ)

Distinct9109
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7853.645
Minimum1
Maximum230106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-03-28T08:15:11.338573image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile43
Q1421
median1772
Q36583
95-th percentile37339
Maximum230106
Range230105
Interquartile range (IQR)6162

Descriptive statistics

Standard deviation18053.237
Coefficient of variation (CV)2.298708
Kurtosis33.416809
Mean7853.645
Median Absolute Deviation (MAD)1608
Skewness5.0070463
Sum2.5244285 × 109
Variance3.2591936 × 108
MonotonicityNot monotonic
2023-03-28T08:15:11.513209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 494
 
0.2%
25 476
 
0.1%
17 466
 
0.1%
9 452
 
0.1%
30 449
 
0.1%
8 442
 
0.1%
14 439
 
0.1%
19 437
 
0.1%
12 429
 
0.1%
26 426
 
0.1%
Other values (9099) 316924
98.6%
ValueCountFrequency (%)
1 376
0.1%
2 404
0.1%
3 383
0.1%
4 379
0.1%
5 405
0.1%
6 389
0.1%
7 373
0.1%
8 442
0.1%
9 452
0.1%
10 346
0.1%
ValueCountFrequency (%)
230106 23
< 0.1%
227966 23
< 0.1%
217955 24
< 0.1%
214513 17
< 0.1%
213535 25
< 0.1%
211593 17
< 0.1%
210432 19
< 0.1%
205347 17
< 0.1%
200603 16
< 0.1%
198527 20
< 0.1%
Distinct10147
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11332.51
Minimum1
Maximum370022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-03-28T08:15:11.690105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile55
Q1558
median2468
Q39334
95-th percentile53129
Maximum370022
Range370021
Interquartile range (IQR)8776

Descriptive statistics

Standard deviation26933.457
Coefficient of variation (CV)2.3766543
Kurtosis36.968916
Mean11332.51
Median Absolute Deviation (MAD)2259
Skewness5.2530899
Sum3.6426539 × 109
Variance7.2541112 × 108
MonotonicityNot monotonic
2023-03-28T08:15:11.864811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 374
 
0.1%
17 363
 
0.1%
52 362
 
0.1%
39 353
 
0.1%
19 352
 
0.1%
24 346
 
0.1%
38 344
 
0.1%
18 340
 
0.1%
11 339
 
0.1%
23 338
 
0.1%
Other values (10137) 317923
98.9%
ValueCountFrequency (%)
1 294
0.1%
2 306
0.1%
3 317
0.1%
4 297
0.1%
5 314
0.1%
6 324
0.1%
7 328
0.1%
8 271
0.1%
9 267
0.1%
10 302
0.1%
ValueCountFrequency (%)
370022 23
< 0.1%
364051 23
< 0.1%
348522 25
< 0.1%
343312 24
< 0.1%
341691 19
< 0.1%
323791 20
< 0.1%
315783 17
< 0.1%
310778 17
< 0.1%
298646 17
< 0.1%
292753 16
< 0.1%

AANTAL_PAT_PER_SPC
Real number (ℝ)

Distinct308
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean684619.67
Minimum1
Maximum1487636
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-03-28T08:15:12.059559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile43722
Q1355515
median764932
Q31026475
95-th percentile1340740
Maximum1487636
Range1487635
Interquartile range (IQR)670960

Descriptive statistics

Standard deviation409428.45
Coefficient of variation (CV)0.59803781
Kurtosis-1.0740056
Mean684619.67
Median Absolute Deviation (MAD)316122
Skewness-0.053160846
Sum2.2006004 × 1011
Variance1.6763165 × 1011
MonotonicityNot monotonic
2023-03-28T08:15:12.241359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
880937 5102
 
1.6%
874110 4354
 
1.4%
843981 4347
 
1.4%
894322 4333
 
1.3%
880489 4273
 
1.3%
897718 4212
 
1.3%
764932 4089
 
1.3%
802099 4026
 
1.3%
1081075 3890
 
1.2%
1100224 3866
 
1.2%
Other values (298) 278942
86.8%
ValueCountFrequency (%)
1 2
 
< 0.1%
2 3
 
< 0.1%
3 4
 
< 0.1%
9 1
 
< 0.1%
10 3
 
< 0.1%
13 4
 
< 0.1%
17 5
 
< 0.1%
28 24
< 0.1%
44 2
 
< 0.1%
146 10
< 0.1%
ValueCountFrequency (%)
1487636 2975
0.9%
1450401 3048
0.9%
1421736 3564
1.1%
1344428 3543
1.1%
1340740 3441
1.1%
1332404 3545
1.1%
1316540 3463
1.1%
1282947 3576
1.1%
1266251 3351
1.0%
1265245 1177
 
0.4%

AANTAL_SUBTRAJECT_PER_SPC
Real number (ℝ)

Distinct309
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1106103.5
Minimum1
Maximum2665360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-03-28T08:15:12.437569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile47348
Q1512567
median1129373
Q31760849
95-th percentile2549091
Maximum2665360
Range2665359
Interquartile range (IQR)1248282

Descriptive statistics

Standard deviation734533.15
Coefficient of variation (CV)0.66407272
Kurtosis-0.77331992
Mean1106103.5
Median Absolute Deviation (MAD)624409
Skewness0.3284368
Sum3.5553927 × 1011
Variance5.3953895 × 1011
MonotonicityNot monotonic
2023-03-28T08:15:12.628664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1211796 5102
 
1.6%
1281512 4354
 
1.4%
1216262 4347
 
1.4%
1315589 4333
 
1.3%
1300454 4273
 
1.3%
1341867 4212
 
1.3%
1155560 4089
 
1.3%
1201838 4026
 
1.3%
2549091 3890
 
1.2%
2665360 3866
 
1.2%
Other values (299) 278942
86.8%
ValueCountFrequency (%)
1 2
 
< 0.1%
2 3
 
< 0.1%
3 4
 
< 0.1%
9 1
 
< 0.1%
10 3
 
< 0.1%
16 4
 
< 0.1%
17 5
 
< 0.1%
28 22
< 0.1%
29 2
 
< 0.1%
44 2
 
< 0.1%
ValueCountFrequency (%)
2665360 3866
1.2%
2659078 3796
1.2%
2619966 3788
1.2%
2594527 3844
1.2%
2549091 3890
1.2%
2480797 3851
1.2%
2179106 3757
1.2%
2062587 3811
1.2%
2052315 1168
 
0.4%
1990256 1167
 
0.4%

GEMIDDELDE_VERKOOPPRIJS
Real number (ℝ)

Distinct3535
Distinct (%)1.3%
Missing48654
Missing (%)15.1%
Infinite0
Infinite (%)0.0%
Mean3588.0168
Minimum70
Maximum287220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-03-28T08:15:12.814308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile140
Q1480
median1260
Q34190
95-th percentile13620
Maximum287220
Range287150
Interquartile range (IQR)3710

Descriptive statistics

Standard deviation6532.4671
Coefficient of variation (CV)1.8206345
Kurtosis145.57442
Mean3588.0168
Median Absolute Deviation (MAD)1030
Skewness7.1813867
Sum9.7873922 × 108
Variance42673126
MonotonicityNot monotonic
2023-03-28T08:15:12.985234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160 2009
 
0.6%
105 1938
 
0.6%
110 1790
 
0.6%
180 1565
 
0.5%
185 1551
 
0.5%
300 1439
 
0.4%
145 1386
 
0.4%
175 1364
 
0.4%
120 1335
 
0.4%
165 1276
 
0.4%
Other values (3525) 257127
80.0%
(Missing) 48654
 
15.1%
ValueCountFrequency (%)
70 226
 
0.1%
75 75
 
< 0.1%
80 362
 
0.1%
85 919
0.3%
90 689
 
0.2%
95 695
 
0.2%
100 924
0.3%
105 1938
0.6%
110 1790
0.6%
115 1093
0.3%
ValueCountFrequency (%)
287220 8
< 0.1%
148910 3
 
< 0.1%
142835 4
< 0.1%
122155 4
< 0.1%
116805 3
 
< 0.1%
109725 7
< 0.1%
108570 7
< 0.1%
107655 4
< 0.1%
101270 8
< 0.1%
96945 5
< 0.1%

Interactions

2023-03-28T08:15:05.123438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:51.769755image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:53.488278image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:55.234661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:56.855584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:58.460955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:00.015560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:01.692917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:03.501860image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:05.320220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:51.972853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:53.679019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:55.426952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:57.049023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:58.646819image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:00.215464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:01.891228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:03.693280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:05.499741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:52.159912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:53.855657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:55.601458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:57.228622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:58.815814image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:00.397052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:02.070889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:03.869649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:05.687662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:52.352837image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:54.032268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:55.780246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:57.403723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:58.988976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:00.584641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:02.254766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:04.047266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:05.869129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:52.539352image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:54.206419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:55.953088image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:57.574047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:59.154547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:00.765280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:02.434854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:04.224822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:06.041172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:52.715034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:54.371427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:56.120973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:57.737366image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:59.312303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:00.941854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:02.605700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:04.391268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:06.229433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:52.910736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:54.559641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:56.305341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:57.921428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:59.491114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:01.130738image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:02.796402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:04.578766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:06.421938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:53.114369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:54.743810image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:56.494648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:58.109713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:59.670738image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:01.325559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:02.982513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:04.763062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:06.600687image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:53.298496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:54.919389image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:56.672442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:58.285367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:14:59.842006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:01.508093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:03.317000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-28T08:15:04.937672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-03-28T08:15:13.138459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
BEHANDELEND_SPECIALISME_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
BEHANDELEND_SPECIALISME_CD1.0000.2170.0080.013-0.061-0.054-0.562-0.4830.049
ZORGPRODUCT_CD0.2171.000-0.142-0.150-0.180-0.212-0.385-0.4150.027
AANTAL_PAT_PER_ZPD0.008-0.1421.0000.9960.3220.3200.0680.078-0.304
AANTAL_SUBTRAJECT_PER_ZPD0.013-0.1500.9961.0000.3190.3210.0710.085-0.306
AANTAL_PAT_PER_DIAG-0.061-0.1800.3220.3191.0000.9870.3100.2930.025
AANTAL_SUBTRAJECT_PER_DIAG-0.054-0.2120.3200.3210.9871.0000.3230.3230.033
AANTAL_PAT_PER_SPC-0.562-0.3850.0680.0710.3100.3231.0000.961-0.013
AANTAL_SUBTRAJECT_PER_SPC-0.483-0.4150.0780.0850.2930.3230.9611.000-0.016
GEMIDDELDE_VERKOOPPRIJS0.0490.027-0.304-0.3060.0250.033-0.013-0.0161.000

Missing values

2023-03-28T08:15:06.984026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-28T08:15:07.695841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

VERSIEDATUM_BESTANDPEILDATUMJAARBEHANDELEND_SPECIALISME_CDTYPERENDE_DIAGNOSE_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
01.02023-03-102023-03-012018-01-0132919598990651021311522852198124168NaN
11.02023-03-102023-03-012018-01-0132919598990661371421522852198124168NaN
21.02023-03-102023-03-012018-01-013291959899067111215228521981241681440.0
31.02023-03-102023-03-012018-01-01329039900290113753798478632198124168545.0
41.02023-03-102023-03-012018-01-013290399002901224224384786321981241681040.0
51.02023-03-102023-03-012018-01-013290399002900258588478632198124168205.0
61.02023-03-102023-03-012018-01-013290399002901017918384786321981241681345.0
71.02023-03-102023-03-012018-01-0132905990029002120124128513462198124168205.0
81.02023-03-102023-03-012018-01-0132905990029011594605128513462198124168545.0
91.02023-03-102023-03-012018-01-01329059900290102562611285134621981241681345.0
VERSIEDATUM_BESTANDPEILDATUMJAARBEHANDELEND_SPECIALISME_CDTYPERENDE_DIAGNOSE_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
3214241.02023-03-102023-03-012017-01-0136111199006100122752106888322128548NaN
3214251.02023-03-102023-03-012017-01-0136111199006101432327521068883221285481340.0
3214261.02023-03-102023-03-012017-01-0136111199006106613147521068883221285483970.0
3214271.02023-03-102023-03-012017-01-0136111199006100411752106888322128548NaN
3214281.02023-03-102023-03-012017-01-01361111990061068227521068883221285484775.0
3214291.02023-03-102023-03-012017-01-0136111199006107033337521068883221285489950.0
3214301.02023-03-102023-03-012017-01-013611119900610672894067521068883221285481660.0
3214311.02023-03-102023-03-012017-01-013611119900610721972117521068883221285486845.0
3214321.02023-03-102023-03-012017-01-0136111199006100822247521068883221285487255.0
3214331.02023-03-102023-03-012017-01-013611119900610185658752106888322128548300.0